可调节相关峰参数的脉冲检测分选算法

针对传统塔康脉冲序列检测算法没有考虑不同工作模式下脉冲间隔（PRI）的特征信息，低信噪比下检测误差大而且算法复杂度高的问题，提出一种可调节参数的基于相关峰峰值位置的脉冲序列检测分选算法.根据相关信号的检测模型确定优化目标，构建一个负定型的埃尔米特检测矩阵；通过对相邻峰值位置的检测滤除不符合PRI特征的脉冲序列；利用脉冲消隐消除衰落效应引起的干扰信号对脉冲检测的影响；采用循环队列的脉冲对分选方法解决非完整周期内脉冲丢失的问题.实验结果表明该检测算法在改善信号信噪比的同时，脉冲到达时间（PTOA）的检测精度和基准脉冲序列的检测准确率都有显著提高. 

Pulse-Sorting Algorithm Based on Correlation Peak Detection with Adjustable Parameters

The conventional pulse sequence detection algorithms do not consider the feature information of the pulse repetition interval (PRI) in different work modes, bring about some disadvantages of larger detection error in low signal-to-noise ratio circumstance and higher computational complexity with TACAN. To solve this problem, an algorithm based on correlation peaks'' positions with adjustable parameters was proposed for pulse sequence detection and sorting. Firstly, determining the optimal objective in virtue of the signal cross-correlation detection model, a negative definite Hermitian Matrix was developed for detecting pulses. The pulse sequences, not conforming to PRIs, were filtered out by detecting the correlation peaks'' positions in the matrix. Then the interference components caused by signal fading were eliminated using pulse blanking. And the problem of missing pulses in incomplete period was solved by using the pulse sort method of cyclic queue. Simulation results show that, the proposed algorithm can improve the detection accuracy of the pulse time of arrival (PTOA) and the detection accuracy of the benchmark pulse sequence while improving the signal-to-noise ratio.